数字图像增强的区域划分迭代函数系统

T. Economopoulos, P. Asvestas, G. Matsopoulos
{"title":"数字图像增强的区域划分迭代函数系统","authors":"T. Economopoulos, P. Asvestas, G. Matsopoulos","doi":"10.1109/IPTA.2012.6469514","DOIUrl":null,"url":null,"abstract":"A new technique is presented for enhancing the contrast of digital images. The proposed method is based on the regional application of the Partitioned Iterated Function Systems (PIFS) algorithm. The subject image is partitioned into domain regions, using a standard Region Growing approach. Each domain region is further partitioned into smaller range regions. In turn, each range region is transformed through a contractive affine spatial transform, as well as through a linear transform of the gray levels of its pixels. The PIFS is used in order to create a lowpass version of the original image, after processing each region. The contrast-enhanced image is obtained by adding the difference of the original image with its lowpass version, to the original image itself. The quantitative and qualitative results obtained, show that the proposed method achieves higher quality image enhancement, compared to two widely used contrast enhancement techniques.","PeriodicalId":267290,"journal":{"name":"2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Regional Partitioned Iterated Function Systems for digital image enhancement\",\"authors\":\"T. Economopoulos, P. Asvestas, G. Matsopoulos\",\"doi\":\"10.1109/IPTA.2012.6469514\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A new technique is presented for enhancing the contrast of digital images. The proposed method is based on the regional application of the Partitioned Iterated Function Systems (PIFS) algorithm. The subject image is partitioned into domain regions, using a standard Region Growing approach. Each domain region is further partitioned into smaller range regions. In turn, each range region is transformed through a contractive affine spatial transform, as well as through a linear transform of the gray levels of its pixels. The PIFS is used in order to create a lowpass version of the original image, after processing each region. The contrast-enhanced image is obtained by adding the difference of the original image with its lowpass version, to the original image itself. The quantitative and qualitative results obtained, show that the proposed method achieves higher quality image enhancement, compared to two widely used contrast enhancement techniques.\",\"PeriodicalId\":267290,\"journal\":{\"name\":\"2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA)\",\"volume\":\"12 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IPTA.2012.6469514\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IPTA.2012.6469514","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

提出了一种增强数字图像对比度的新技术。该方法基于分区迭代函数系统(PIFS)算法的区域应用。使用标准的区域增长方法将主题图像划分为域区域。每个域区域进一步划分为更小的范围区域。然后,通过压缩仿射空间变换变换每个范围区域,以及通过其像素的灰度级的线性变换。使用PIFS是为了在处理每个区域后创建原始图像的低通版本。对比度增强图像是将原图像与低通图像的差值加到原图像上得到的。定量和定性结果表明,与两种常用的对比度增强技术相比,该方法实现了更高质量的图像增强。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Regional Partitioned Iterated Function Systems for digital image enhancement
A new technique is presented for enhancing the contrast of digital images. The proposed method is based on the regional application of the Partitioned Iterated Function Systems (PIFS) algorithm. The subject image is partitioned into domain regions, using a standard Region Growing approach. Each domain region is further partitioned into smaller range regions. In turn, each range region is transformed through a contractive affine spatial transform, as well as through a linear transform of the gray levels of its pixels. The PIFS is used in order to create a lowpass version of the original image, after processing each region. The contrast-enhanced image is obtained by adding the difference of the original image with its lowpass version, to the original image itself. The quantitative and qualitative results obtained, show that the proposed method achieves higher quality image enhancement, compared to two widely used contrast enhancement techniques.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Case study: Deployment of the 2D NoC on 3D for the generation of large emulation platforms A combining approach for 2D face recognition application on IV2 database Spherical coordinates framed RGB color space dichromatic reflection model based image segmentation: Application to wildland fires' outlines extraction Image processing and vision for the study and the modeling of spreading fires Real time watermarking to authenticate the WSQ bitstream
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1